Plant Scaffolds Support Motor Recovery and Regeneration in Rats after Traumatic Spinal Cord Injury
Bibliographic record
Abstract
ABSTRACT As of yet, no standard of care incorporates biomaterials to treat traumatic spinal cord injury (SCI). However, intense development of biomaterials for treating SCI has focused on fabricating microscale channels to support the regrowth of axons while minimizing scar formation. We previously demonstrated that plant tissues could be decellularized and processed to form sterile, biocompatible and implantable biomaterials that support cell infiltration and vascularization in vivo . Vascularized plant tissues contain continuous microscale channels with geometries relevant for supporting neural regeneration. We hypothesized that decellularized vascular bundles would support neural regeneration and motor recovery in SCI. Sprague Dawley rats received a complete T8-T9 spinal cord transection and were implanted with acellular plant-derived scaffolds and allowed to recover over 28 weeks. Animals that received the scaffolds alone, with no other therapeutic compounds, demonstrated a significant and stable partial improvement in motor function compared to control animals as early as week 4 post-injury. Hind-limb motor function did not deteriorate over the remaining 28 weeks. Histological analysis revealed minimal astrocyte scarring at the spinal cord - scaffold interface, aligned axonal projection through the scaffolds, populations of serotonergic neurons and Schwann cells, laminin and collagen deposition and the presence of blood vessels. Axonal reconnection via the scaffold was also confirmed by Fluro-gold retrograde tracing. Taken together, our work defines a novel route for building upon naturally occurring plant microarchitectures to support the repair of the spinal cord post-injury. Notably, these results were achieved without the use of growth factors, stem/progenitor cells, or any other interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".